Internet-of-things (IoT)-based data collection and analytics for onsite scaffolding project management

脚手架作为建筑工地上的一种临时结构，为工匠提供进入结构的某些区域以进行建筑活动的通道。根据项目的规模，脚手架预算可以占总直接工作预算的很大一部分。脚手架活动的管理失败可能导致项目成本超支和进度延误。同时，脚手架管理因其性质而具有挑战性，例如，在不同地点消耗各种材料成分的特殊日常活动。这使得脚手架管理变得困难，从跟踪正在进行的活动和材料，到有效地利用跟踪的信息进行项目管理。为了改善当前脚手架管理的行业实践，需要及时准确的数据采集机制来记录脚手架数据，以进一步形成用于项目管理目的的生产率指标。因此，本研究计划旨在开发一个数据驱动的框架，用于现场脚手架数据采集和分析利用，为项目管理提供决策支持信息。新玩法大学的研究团队将与欣顿脚手架解决方案（加拿大安大略）合作，开发基于物联网（IoT）的解决方案，该解决方案将机器学习和统计方法结合起来，形成项目挣值分析（伊娃）的生产力指标。具体而言，该研究项目将：（1）开发基于物联网的移动的应用程序，用于脚手架数据收集;（2）开发数据清理和分类机制;（3）使用贝叶斯更新和回归建模开发生产力分析模块;（4）基于开发的生产力指标开发伊娃;以及（5）开发数据可视化的仪表板系统。该项目将通过数据分析为合作伙伴公司提供项目见解，并为公司的运营团队提供敏捷的数字解决方案，以实现持续的数据收集和项目监控。该项目将有助于加拿大的建筑数字技术创新，最终提高行业的效率，安全性和质量，提高竞争力。

Scaffolding, as a type of temporary structure on construction sites, provides access for tradespeople to certain areas of a structure to perform construction activities. Depending on a project's size, the scaffolding budget can account for a substantial amount of the total direct work budget. Failure in the management of scaffolding activities can result in cost overrun and schedule delays in the projects. Meanwhile, scaffolding management is challenging by its nature, e.g., ad-hoc trivial daily activities with various material components consumed at different locations. This makes scaffolding management difficult, from tracking ongoing activities and materials, to effectively utilizing the tracked information for project management. To improve the current industry practice of scaffolding management, timely and accurate data acquisition mechanisms are necessary to record scaffolding data to further form productivity metrics for project management purposes. Therefore, this research project intends to develop a data-driven framework for onsite scaffolding data acquisition and utilization of analytics to provide decision support information for project management. The research team at the University of New Brunswick will collaborate with Hinton Scaffolding Solutions (Ontario, Canada) to develop Internet-of-things (IoT)-based solutions that incorporate machine learning and statistical methods to form productivity metrics for project earned value analysis (EVA). Specifically, the research project will: (1) develop IoT-based mobile applications for scaffolding data collection; (2) develop data cleaning and classification mechanisms; (3) develop productivity analysis modules using Bayesian updating and regression modeling; (4) develop EVA based on the developed productivity metrics; and (5) develop dashboard systems for data visualization. This project will provide project insights for the partner company through data analytics and will equip the company's operations team with agile digital solutions for continuous data collection and project monitoring. The project will contribute to Canada's construction digital technologies innovation, which ultimately increases the efficiency, safety, and quality of the industry with improved competitiveness.